用于监测风力涡轮机传动系轴承的字典学习方法
Dictionary learning approach to monitoring of wind turbine drivetrain bearings
摘要：
由于风力发电场具有挑战性的运行条件、快速的技术发展和大量老化的风力涡轮机，状态监测是风力发电场高效运行的核心。特别是，预测性维护计划需要早期检测故障，误报很少。实现这种类型的检测是一个具有挑战性的问题，由于一些故障，特别是发生在一些传动系轴承的故障的复杂和弱的签名。在这里，我们研究了最近提出的基于无监督字典学习的状态监测方法，使用在典型工业操作下记录的超过46个月的振动数据。因此，我们贡献了新的测试结果和公开的真实世界数据。以前使用字典学习解决状态监测任务的研究结果表明，无监督特征学习对于诊断和异常检测是有用的。然而，这些研究是基于在受控条件下运行的测试装置的小组标记数据，这些数据集中在分类任务上，这对于定量方法比较很有用，但对这些方法在实践中的有用程度却知之甚少。在这项研究中，字典是从六个不同的涡轮机齿轮箱振动学习，字典随后传播了几年的监测数据时，故障发生。我们使用两种不同的稀疏编码算法进行实验，以研究所选择的算法是否会影响异常情况的特征。我们计算初始字典和传播字典之间的字典距离，并找到异常字典自适应的时间段，从传动系轴承更换前六个月开始，到变速箱更换前一年开始。
英文摘要：
Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type of detection is a challenging problem due to the complex and weak signatures of some faults, particularly the faults that occur in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded over 46 months under typical industrial operations. Thus, we contribute novel test results and real world data that are made publicly available. The results of former studies addressing condition monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on small sets of labeled data from test rigs operating under controlled conditions that focus on classification tasks, which are useful for quantitative method comparisons but gives little insight into how useful these approaches are in practice. In this study, dictionaries are learned from gearbox vibrations in six different turbines, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We perform the experiment using two different sparse coding algorithms to investigate if the algorithm selected affects the features of abnormal conditions. We calculate the dictionary distance between the initial and propagated dictionaries and find the time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement.
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